{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model parameters selection\n",
    "\n",
    "Select the parameters for Unet architecture to find first break picking time on the seismic trace.\n",
    "\n",
    "* [The parameters studied](#The-parameters-studied)\n",
    "* [Parameters value area](#Parameters-value-area)\n",
    "* [Metrics](#Metrics)\n",
    "* [Dataset loading](#Dataset-loading)\n",
    "* [Dataset bypass](#Dataset-bypass)\n",
    "* [Creating a research object](#Creating-a-research-object)\n",
    "* [Results](#Results)\n",
    "* [Conclusion](#Conclusion)\n",
    "\n",
    "    \n",
    "### The parameters studied\n",
    "The number of UNet blocks and the number of filters and kernel size for encoder convolutions.\n",
    "\n",
    "### Parameters value area\n",
    "* _Number of Unet blocks_ - 4, 5, 7.\n",
    "\n",
    "* _Encoder convolution kernel size_ - 3, 5\n",
    "                       \n",
    "### Metrics\n",
    "Mean absolute error between predicted picking and target.\n",
    "\n",
    "### Dataset loading\n",
    "Dataset is given by SEGY file with a seismograms and CSV file with target picking time for this SEGY. The seismorgam contains 750K traces combined in 1001 field records. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "from matplotlib import colors as mcolors\n",
    "\n",
    "sys.path.append('../../..')\n",
    "\n",
    "from seismicpro.batchflow import Pipeline, B, V, C, W\n",
    "from seismicpro.batchflow.models.torch import UNet\n",
    "from seismicpro.src import (SeismicDataset, FieldIndex, TraceIndex, seismic_plot,\n",
    "                            show_research, print_results)\n",
    "from seismicpro.batchflow.research import Research, Option"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we index field records:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_raw = '/data/FB/dataset_1/Pal_Flatiron_1k.sgy'\n",
    "markup_path='/data/FB/dataset_1/Pal_Flatiron_1k_picking.csv'\n",
    "\n",
    "index = FieldIndex(name='raw', path=path_raw, markup_path=markup_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset bypass:\n",
    "* Define model config\n",
    "* Define index and train / test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs_config = {\n",
    "    'raw': {'shape': (1, W(B('raw')).shape[2])},\n",
    "    'mask': {'shape': (1, W(B('raw')).shape[2]),\n",
    "              'classes': 2}\n",
    "    }\n",
    "\n",
    "config = {\n",
    "    'inputs': inputs_config,\n",
    "    'initial_block/inputs': 'raw',\n",
    "    'optimizer': ('Adam', {'lr': 0.001}),\n",
    "    'head/num_classes': 2, \n",
    "    'body/num_blocks': 5,\n",
    "    'body/filters': C('filters'),\n",
    "    'body/encoder': dict(layout='cna cna', kernel_sizee=C('kernel_size')),\n",
    "    'body/decoder': dict(layout='cna cna', kernel_size=C('kernel_size')),\n",
    "    'body/downsample' : dict(layout='p', pool_size=2, pool_strides=2, padding=1),\n",
    "    'device': C('device'),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_index = index.create_subset(index.indices[:10])\n",
    "train_data = SeismicDataset(TraceIndex((train_index)))\n",
    "\n",
    "test_index = index.create_subset(index.indices[10:12])\n",
    "test_data = SeismicDataset(TraceIndex((test_index)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create an instance of train and test pipelines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "B_SIZE = 64\n",
    "train_pipeline = (Pipeline()\n",
    "                      .load(components='raw', fmt='segy')\n",
    "                      .load(components='markup', fmt='picks')\n",
    "                      .standardize(src='raw', dst='raw')\n",
    "                      .picking_to_mask(src='markup', dst='mask')\n",
    "                      .init_model('dynamic', UNet, 'my_model', config)\n",
    "                      .init_variable('loss', init_on_each_run=list)\n",
    "                      .apply_transform_all(src='raw', dst='raw', func=lambda x: np.stack(x))\n",
    "                      .apply_transform_all(src='mask', dst='mask', func=lambda x: np.stack(x))\n",
    "                      .train_model('my_model', B('raw'), B('mask'), \n",
    "                                   fetches='loss', save_to=V('loss',mode='w'))\n",
    "                      .run_later(B_SIZE, n_epochs=None, drop_last=True, shuffle=21))\n",
    "\n",
    "test_pipeline = (Pipeline()\n",
    "                      .import_model('my_model', C('import_from'))\n",
    "                      .init_variable('true', init_on_each_run=list())\n",
    "                      .init_variable('predictions', init_on_each_run=list())\n",
    "                      .load(components='raw', fmt='segy')\n",
    "                      .load(components='markup', fmt='picks')\n",
    "                      .standardize(src='raw', dst='raw')\n",
    "                      .picking_to_mask(src='markup', dst='mask')\n",
    "                      .update_variable('true', B('mask'), mode='a')\n",
    "                      .add_components(components='predictions')\n",
    "                      .apply_transform_all(src='raw', dst='raw', func=lambda x: np.stack(x))\n",
    "                      .apply_transform_all(src='mask', dst='mask', func=lambda x: np.stack(x))\n",
    "                      .predict_model('my_model', B('raw'),\n",
    "                                     fetches='predictions',  save_to=B('predictions', mode='a'))\n",
    "                      .mask_to_pick(src='predictions', dst='predictions', labels=False)\n",
    "                      .update_variable('predictions', B('predictions'), mode='a')\n",
    "                      .run_later(100, n_epochs=1, drop_last=False, shuffle=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define of auxiliary functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mae(iteration, experiment, pipeline):\n",
    "    pipeline = experiment[pipeline].pipeline\n",
    "    pred = np.hstack(np.concatenate(pipeline.get_variable('predictions')))\n",
    "    true = np.argmax(np.stack(np.concatenate(pipeline.get_variable('true'))), axis=1)\n",
    "    return np.mean(np.abs(true - pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a research object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the grid of parameters for search as well as research object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = Option('filters', [[8, 16, 32, 64],\n",
    "                          [8, 16, 32, 64, 128],\n",
    "                          [8, 16, 32, 64, 128, 128, 256]]) * Option('kernel_size', [3, 5]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "research = (Research()\n",
    "    .add_pipeline(train_pipeline, dataset=train_data, variables='loss', name='train')\n",
    "    .add_pipeline(test_pipeline, dataset=test_data, name='test', run=True, execute=10, import_from='train')\n",
    "    .add_grid(grid)\n",
    "    .add_function(mae, returns='mae', name='test_mae', execute=10, pipeline='test')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it's time to train research instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_REPEAT = 5\n",
    "NUM_ITERS = 1000\n",
    "\n",
    "research.run(NUM_REPEAT, NUM_ITERS, workers=1, name='research', gpu=[3, 4, 5, 6, 7], bar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results\n",
    "\n",
    "Loss functions vs a number of iterations for each parameters set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = research.load_results(use_alias=True)\n",
    "show_research(df, layout=['train/loss', 'test_mae/mae'], average_repetitions=True, color=list(mcolors.TABLEAU_COLORS.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test metrics avaraged on the last 20 iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_mae_mean</th>\n",
       "      <th>test_mae_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>filters_[8, 16, 32, 64, 128, 128, 256]-kernel_size_3</th>\n",
       "      <td>5.727922</td>\n",
       "      <td>0.038726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filters_[8, 16, 32, 64, 128, 128, 256]-kernel_size_5</th>\n",
       "      <td>8.456002</td>\n",
       "      <td>0.058169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filters_[8, 16, 32, 64, 128]-kernel_size_3</th>\n",
       "      <td>12.697358</td>\n",
       "      <td>0.088352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filters_[8, 16, 32, 64, 128]-kernel_size_5</th>\n",
       "      <td>8.994951</td>\n",
       "      <td>0.062346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filters_[8, 16, 32, 64]-kernel_size_3</th>\n",
       "      <td>11.506302</td>\n",
       "      <td>0.080525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filters_[8, 16, 32, 64]-kernel_size_5</th>\n",
       "      <td>24.851517</td>\n",
       "      <td>0.175043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    test_mae_mean  \\\n",
       "filters_[8, 16, 32, 64, 128, 128, 256]-kernel_s...       5.727922   \n",
       "filters_[8, 16, 32, 64, 128, 128, 256]-kernel_s...       8.456002   \n",
       "filters_[8, 16, 32, 64, 128]-kernel_size_3              12.697358   \n",
       "filters_[8, 16, 32, 64, 128]-kernel_size_5               8.994951   \n",
       "filters_[8, 16, 32, 64]-kernel_size_3                   11.506302   \n",
       "filters_[8, 16, 32, 64]-kernel_size_5                   24.851517   \n",
       "\n",
       "                                                    test_mae_std  \n",
       "filters_[8, 16, 32, 64, 128, 128, 256]-kernel_s...      0.038726  \n",
       "filters_[8, 16, 32, 64, 128, 128, 256]-kernel_s...      0.058169  \n",
       "filters_[8, 16, 32, 64, 128]-kernel_size_3              0.088352  \n",
       "filters_[8, 16, 32, 64, 128]-kernel_size_5              0.062346  \n",
       "filters_[8, 16, 32, 64]-kernel_size_3                   0.080525  \n",
       "filters_[8, 16, 32, 64]-kernel_size_5                   0.175043  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = print_results(df, 'test_mae/mae', True,ascending=True, n_last=20)\n",
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see, that the best quality shows model with following parameters:\n",
    "* _Number of Unet blocks_ - 7.\n",
    "* _Kernel size_ - 3."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
